code: def parse_args():
      parser = argparse.ArgumentParser(
          description="Eval baseline RLAgent"
      )
      parser.add_argument(
          "--load_path",
          default="./saved_files",
          help="The path to the model [.h5]",
      )
      parser.add_argument(
          "--logs_path",
          required=False,
          default="./logs_path",
          type=str,
          help="Path to output logs directory",
      )
      parser.add_argument(
          "--nb_episode",
          required=False,
          default=1,
          type=int,
          help="Number of episodes to evaluate",
      )
      parser.add_argument(
          "--nb_process",
          required=False,
          default=1,
          type=int,
          help="Number of cores to use",
      )
      parser.add_argument(
          "--max_steps",
          required=False,
          default=-1,
          type=int,
          help="Maximum number of steps per scenario",
      )
      parser.add_argument(
          "--save_gif", action="store_true", help="Enable GIF Output"
      )
      parser.add_argument(
          "--verbose", action="store_true", help="Verbose runner output"
      )
      return parser.parse_args()
  
file_path: PARL/benchmark/torch/NeurIPS2020-Learning-to-Run-a-Power-Network-Challenge/track1/evaluate.py
imports: import torch
  import os
  from grid2op.Runner import Runner
  from l2rpn_baselines.utils.save_log_gif import save_log_gif
  from rl_agent import RLAgent
  import argparse
  import grid2op
  from lightsim2grid.LightSimBackend import LightSimBackend
label_desc: Use image node_id 1 for calling a global function with example usage: parse_args() and returns: parser